Sri Rajagopalan: Can you connect Sigmoid’s capabilities directly with Reckitt’s eCommerce results?
Sigmoid helps us normalize all the campaign data into a single place. The team provides us with access to real-time dashboards that basically break down channel performance at a creative level, mark the audience, plan our investments and identify the aspects that are really working for us. On the e-commerce front, we examine Amazon sales data that are not attributed to the media, to understand how it is impacting our organic sales. We can then optimize overall media investment to generate both offline and online sales by building relevancy and achieving organic sales. Sigmoid’s model helps us generate bi-weekly insights in a cost efficient manner to visualize the impact of activation. In this way, we’re able to successfully measure the impact of our digital campaigns on offline sales.
Sri Rajagopalan: What does AI mean in the CPG industry from an ‘outcome’ perspective and how does Sigmoid leverage AI?
Rahul Singh: Over the years, AI is adding value to the CPG industry and is bringing new initiatives that power insights into promotions, marketing, stocks, distribution, and more. Having accurate and granular AI models can drive incremental revenue growth by more than 10%.
Another core area where AI drives significant value is creating hyper personalized consumer experiences such as pricing or promotions. We have built an AI powered consumer intelligence engine that constantly ingests real time data and measures metadata related to consumer behavior, decision making, preferences, and interests to generate relevant recommendations to maximize value for businesses.
In addition, we have developed capabilities to build intelligent models at a single consumer level that get self-optimized to learn about consumers and identify most appropriate marketing techniques.
Taj Peeran: The basic principle of AI and machine learning is to become significant business drivers. AI models like that of Sigmoid can help deliver hyper-personalized consumer experiences for the website recommending similar products to upsell or cross sell. Additionally, CPG companies are starting to realize the benefits of creative optimization where machine learning algorithms help deliver dynamic creatives or ads based on consumer’s interaction with other elements, in real time without manual intervention, which is quite innovative.
Sri Rajagopalan: How are you driving value for your clients in Customer Life-Time Value (CLTV) and Hyper-Personalization?
Rahul Singh: Personalized recommendations that drive better Customer Life-Time Value (CLTV) is an important initiative for every consumer-focused brand to retain and improve their top line growth. CLTV uses a lot of different approaches and personalized recommendations. Personalized recommendations can be of two types— customizing recommendations at a segment-level and treating a single consumer as a unique individual. We have carried out personalized recommendations with multiple Fortune 500 companies catering to both approaches, depending on their level of maturity and availability of either the data or the channels that they are targeting. In one such instance, we built a robust system for a global Fortune 500 company to improve their CLTV by defining the ML models and then understanding the current and future potential of a consumer’s value. We helped them identify consumers as high potential to build stronger and better relationships to improve their lifetime value.